I have two time-courses. Both are the same length. Both are univariate. Each represents the average EEG signal from a unique subgroup. The two subgroups do not have the same number of subjects.
I would like to find individual time-points where the difference between the two groups is statistically significant.
It seems as though my options are:
- Perform a two-tailed t-test for each time-point and correct for multiple comparisons.
- Perform an ANOVA analysis.
- Use a GLM to model each time-course and then analyze the beta coefficients
However, there seem to be drawbacks to each approach.
- Given the length of the EEG time-course, and the relatively small difference between the two values, I worry about the risk of false negatives in the case of t-tests.
- ANOVA can tell me if a given time-point is different, but does not identify the actual time-point of interest. Additionally, the assumption of independence would be violated because sequential measurements will be correlated.
- Linear analysis would not identify time-points of interest.